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AI Makerspace MCP Demo Server

by lalrow

animal_fact

Retrieve interesting facts about animals by specifying a species name. This tool provides educational information for learning about wildlife characteristics and behaviors.

Instructions

Get a fun fact about a given animal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
animalNodog

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • server.py:69-78 (handler)
    The main handler function for the 'animal_fact' tool. It takes an optional animal name (default 'dog'), fetches a fun fact from 'https://some-random-api.com/facts/{animal}', and returns the fact or an error message.
    def animal_fact(animal: str = "dog") -> str:
        """Get a fun fact about a given animal."""
        try:
            url = f"https://some-random-api.com/facts/{animal.lower()}"
            r = requests.get(url)
            if r.status_code == 200:
                return r.json().get("fact", "No fact found.")
            return f"⚠️ API error: {r.status_code}"
        except Exception as e:
            return f"❌ Something went wrong: {e}"
  • server.py:68-68 (registration)
    The @mcp.tool() decorator registers the 'animal_fact' function as an MCP tool.
    @mcp.tool()
  • Tool schema defined via function signature (input: animal str default 'dog', output: str) and docstring.
    def animal_fact(animal: str = "dog") -> str:
        """Get a fun fact about a given animal."""
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves a 'fun fact', implying a read-only operation, but doesn't address potential traits like rate limits, error handling, or data sources. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core purpose, making it easy to parse and understand quickly, which is ideal for conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one optional parameter) and the presence of an output schema (which handles return values), the description is adequate but incomplete. It covers the basic purpose but lacks details on usage guidelines, behavioral traits, and parameter specifics, making it minimally viable but with clear gaps in context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the description adds minimal value by implying the 'animal' parameter is used to specify the subject. However, it doesn't clarify acceptable animal names, formats, or constraints, so it only partially compensates for the schema's lack of documentation, aligning with the baseline for moderate coverage gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('fun fact about a given animal'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'science_term' or 'space_fact', which might also provide facts but about different domains, so it doesn't reach the highest score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'number_fact' or 'web_search', nor does it specify contexts where animal facts are preferred over other types of information, leaving the agent to infer usage based on the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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